Comparative genomic analysis provides insight into the phylogeny and potential mechanisms of adaptive evolution of Sphingobacterium sp. CZ-2.

Sphingobacterium is a class of Gram-negative, non-fermentative bacilli that have received widespread attention due to their broad ecological distribution and oil degradation ability, but are rarely involved in infections. In this manuscript, a novel Sphingobacterium strain isolated from wildfire-infected tobacco leaves was named Sphingobacterium sp. CZ-2. NGS and TGS sequencing results showed a whole genome of 3.92 Mb with 40.68 mol% GC content and containing 3,462 protein-coding genes, 9 rRNA-coding genes and 50 tRNA-coding genes. Phylogenetic analysis, ANI and dDDH calculations all supported that Sphingobacterium sp. CZ-2 represented a novel species of the genus Sphingobacterium. Analysis of the specific genes of Sphingobacterium sp. CZ-2 by comparative genomics revealed that metal transport proteins encoded by the troD and cusA genes could maintain the balance of heavy metal ion concentrations in the internal environment of bacteria and avoid heavy metal toxicity while meeting the needs of growth and reproduction, and transport proteins encoded by the malG gene could keep nutrients required for the survival of bacteria. Synteny and genome evolutionary analyses of Sphingobacterium strains implicated that the gene family contraction as a major process in genome evolution, with insertional sequences leading to mutations, deletions and reversals of genes that help bacteria to withstand complex environmental changes. Complete genome sequencing and systematic comparative genomic analysis will contribute new insights into the adaptive evolution of this novel species and the genus Sphingobacterium.

iPReditor-CMG: Improving a predictive RNA editor for crop mitochondrial genomes using genomic sequence features and an optimal support vector machine.

In crops, RNA editing is one of the most important post-transcriptional processes in which specific cytidines (C) in virtually all mitochondrial protein-coding genes are converted to uridines (U). Despite extensive recent research in RNA editing, exploring all of the C-to-U editing events efficiently on the genomic scale remains challengeable. Developing accurate prediction methods for the detection of RNA editing sites would dramatically reduce experimental determination. Therefore, we propose a novel method, iPReditor-CMG (improved predictive RNA editor for crop mitochondrial genomes), to predict crop mitochondrial editing sites using genome sequence and an optimised support vector machine (SVM). We first selected three mitochondrial genomes with known RNA editing sites from Arabidopsis thaliana, Brassica napus and Oryza sativa, released by NCBI, as the training and test sets. The genes and their transcripts from self-sequenced tobacco mitochondrial ATPase were selected as the validation set. The iPReditor-CMG first coded the genome sequences as numerical vectors and then performed an efficient feature selection on the high-dimensional feature space, where the SVM was employed in feature selection and following modelling. The average independent prediction accuracy of intraspecific editing sites across three species was 0.85, and up to 0.91 in A. thaliana, which outperformed the reference models. For the interspecific independent prediction, the prediction accuracy between dicotyledons was 0.78 and the accuracy between dicotyledons and monocotyledons was 0.56, which implies that there might be similarity in the C-to-U editing mechanism in close relatives. Finally, the best model was identified with an independent test accuracy of 0.91 and an AUC of 0.88, which suggested that five unreported feature sequences, i.e. TGACA, ACAAC, GTAGA, CCGTT and TAACA, are closely associated with the editing phenomenon. Multiple tests supported that the iPReditor-CMG could be effectively applied to predict editing sites in crop mitochondria, which may further contribute to understanding the mechanisms of site editing and post-transcriptional events in crop mitochondria.